Bolla Spectral Clustering and Biclustering

Learning Large Graphs and Contingency Tables
1. Auflage 2013
ISBN: 978-1-118-65070-7
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 296 Seiten, E-Book

Explores regular structures in graphs and contingency tables by spectral theory and statistical methods
This book bridges the gap between graph theory and statistics by giving answers to the demanding questions which arise when statisticians are confronted with large weighted graphs or rectangular arrays. Classical and modern statistical methods applicable to biological, social, communication networks, or microarrays are presented together with the theoretical background and proofs.
This book is suitable for a one-semester course for graduate students in data mining, multivariate statistics, or applied graph theory; but by skipping the proofs, the algorithms can also be used by specialists who just want to retrieve information from their data when analysing communication, social, or biological networks.
Spectral Clustering and Biclustering:
* Provides a unified treatment for edge-weighted graphs and contingency tables via methods of multivariate statistical analysis (factoring, clustering, and biclustering).
* Uses spectral embedding and relaxation to estimate multiway cuts of edge-weighted graphs and bicuts of contingency tables.
* Goes beyond the expanders by describing the structure of dense graphs with a small spectral gap via the structural eigenvalues and eigen-subspaces of the normalized modularity matrix.
* Treats graphs like statistical data by combining methods of graph theory and statistics.
* Establishes a common outline structure for the contents of each algorithm, applicable to networks and microarrays, with unified notions and principles.

Autoren/Hrsg.

Bolla, Marianna

Über Autor(innen)

She is graduated from the Eötvös University ofBudapest and holds a PhD (1984); further, a CSc degree (1993) fromthe Hungarian Academy of Sciences. Currently, she is a professor ofthe Institute of Mathematics, Budapest University of Technology andEconomics and adjoint professor of the Central European Universityof Budapest. She also leads an undergraduate research course onSpectral Clustering in the Budapest Semester of Mathematics.
Her fields of expertise are multivariate statistics, appliedgraph theory, and data mining of social, biological, andcommunication networks. She has been working in various nationaland European research projects related to networks and dataanalysis.
She has published research papers in the Journal of MultivariateAnalysis, Linear Algebra and Its Applications, DiscreteMathematics, Discrete Applied Mathematics, European Journal ofCombinatorics, and the Physical Review E, among others.
She is the coauthor of the textbook in Hungarian: Bolla, M.,Krámli, A., Theory of statistical inference, Typotex, Budapest(first ed. 2005, second ed. 2012) and another Hungarian book onmultivariate statistical analysis. She was the managing editor ofthe book Contests in Higher Mathematics (ed. G. J. Székely),Springer, 1996.